Abstract: Various embodiments are provided for cognitive data curation in an Internet of Things (IoT) computing environment by a processor. Each data flow and mapping of the data flows may be related to one or more concepts and relationships between the one or more concepts. One or more inconsistencies may be identified between those data flows used to answer a query for time-series data pertaining to the one or more concepts. The inconsistencies between those of the plurality of data flows may be corrected using inference and reasoning via a machine learning operation.

Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph of heterogeneous data source relationships, a plurality of data flows between one or more heterogeneous data sources relating to the query, and an ontology of concepts and representing a domain knowledge of the one or more heterogeneous data sources.

Abstract: In one aspect, a method for reducing uncertainty in a hydraulic model of a water distribution network due to uncertain parameters and faults in the water distribution network is provided which includes the steps of: (i) calculating an optimized placement of sensors throughout a given uncertain section of the water distribution network; (ii) collecting data from the sensors; (iii) partitioning the given uncertain section of the water distribution network into observable and unobservable sub-sections based on the hydraulic model and a) a position, b) a number, and/or c) a type of the sensors that are available; (iv) correcting uncertain parameters and identifying faults for each of the observable sub-sections; (v) calculating a global uncertainty value for each of the unobservable sub-sections; and (vi) repeating the steps (i)-(vi) iteratively, at each iteration selecting an uncertain sub-section of the water distribution network, until no uncertain sub-sections of the water distribution network remain.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data based, at least in part, on one or more similar consumption patterns of meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: A system for posterior estimation of variables. Receiving a set of data inputs. Determining a first model of the water distribution network based on the set of data inputs. Determining a second model of the water distribution network based on the set of data inputs, and the first model.

Abstract: A method includes loading a guest virtual machine onto a host system, determining, with the host system, an encapsulation method to be used in association with a virtual network associated with the guest virtual machine, determining an overhead value based on the encapsulation method, determining an adjusted maximum transmission unit (MTU) value based on the overhead value, and passing, information related to the adjusted MTU value from the host system to the guest virtual machine.

Abstract: A system for posterior estimation of variables. Receiving a set of data inputs. Determining a first model of the water distribution network based on the set of data inputs. Determining a second model of the water distribution network based on the set of data inputs, and the first model.

Abstract: A fluid is modeled as a set of discrete particles. Each of the particles is associated with one or more properties, and a spatial distance comprising a smoothing length over which the one or more properties are to be smoothed. A corresponding trajectory is simulated for each of the particles. The corresponding trajectory is used to formulate a first solution for simulating transport within the fluid. A first predicted error is determined for the first solution. An iterative adjustment is performed to at least one of: a quantity of particles, the smoothing length, or the one or more corresponding properties, to formulate a second solution for simulating transport with the fluid, and a second predicted error is determined for the second solution, until the second predicted error is within a predefined boundary.

Abstract: Embodiments for providing aquatic epidemic alerts by a processor are described. Information associated with an aquatic epidemic in a body of water is received. A threat level associated with the aquatic epidemic for a location within the body of water is determined based on the information associated with the aquatic epidemic and water flow data associated with the body of water. An indication of the threat level is generated.

Abstract: Embodiments for automated data exploration and validation by a processor. One or more optimal data flows are provided in response to a query for one or more heterogeneous data sources according to an inference model based on a knowledge graph of heterogeneous data source relationships, a plurality of data flows between one or more heterogeneous data sources relating to the query, and an ontology of concepts and representing a domain knowledge of the one or more heterogeneous data sources.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data based, at least in part, on one or more similar consumption patterns of meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: Embodiments for providing aquatic epidemic alerts by a processor are described. Information associated with an aquatic epidemic in a body of water is received. A threat level associated with the aquatic epidemic for a location within the body of water is determined based on the information associated with the aquatic epidemic and water flow data associated with the body of water. An indication of the threat level is generated.

Abstract: An apparatus sets a layer counter to point to a first layer of a data structure. The apparatus determines the layer counter to reference an overflowing cell. The apparatus increments the layer counter to point to a second layer of the data structure. The apparatus determines the incremented layer counter to reference a non-overflowing cell. The apparatus increments a value of the non-overflowing cell, wherein the first layer is stored in a first cache and the second layer is stored in a second cache, and wherein the first cache differs from the second cache with respect to one or more of speed or size.

Abstract: A method for matching first elements with second elements. Each of the first elements and second elements is a character string. The method comprises: calculating a first integer hash value for each of the first elements using a string hash function, wherein the first integer hash value is an output integer calculated from using each of the first elements as an input character string of the function; calculating second integer hash values for each of the second elements using the function; grouping each of the first elements into at least one group of a set of blocking groups using its first integer hash value; grouping each of the second elements into at the least one group of the set of blocking groups using its second integer hash value; and matching first elements with second elements within each group of the set of blocking groups using a string comparison function.

Abstract: Generating actionable information is provided. A plurality of different types of data corresponding to a set of customers of a service is collected via a network. A list of customers is generated from the set of customers of the service that are likely to take an action corresponding to a business goal. The list of customers is based on a linked set of labels corresponding to the business goal for a subset of customers within the set of customers of the service. The actionable information corresponding to customers within the list of customers is generated. An action step is performed based on the generated actionable information corresponding to the customers within the list of customers.

Abstract: In one aspect, a method for reducing uncertainty in a hydraulic model of a water distribution network due to uncertain parameters and faults in the water distribution network is provided which includes the steps of: (i) calculating an optimized placement of sensors throughout a given uncertain section of the water distribution network; (ii) collecting data from the sensors; (iii) partitioning the given uncertain section of the water distribution network into observable and unobservable sub-sections based on the hydraulic model and a) a position, b) a number, and/or c) a type of the sensors that are available; (iv) correcting uncertain parameters and identifying faults for each of the observable sub-sections; (v) calculating a global uncertainty value for each of the unobservable sub-sections; and (vi) repeating the steps (i)-(vi) iteratively, at each iteration selecting an uncertain sub-section of the water distribution network, until no uncertain sub-sections of the water distribution network remain.

Abstract: In one aspect, a method for reducing uncertainty in a hydraulic model of a water distribution network due to uncertain parameters and faults in the water distribution network is provided which includes the steps of: (i) calculating an optimized placement of sensors throughout a given uncertain section of the water distribution network; (ii) collecting data from the sensors; (iii) partitioning the given uncertain section of the water distribution network into observable and unobservable sub-sections based on the hydraulic model and a) a position, b) a number, and/or c) a type of the sensors that are available; (iv) correcting uncertain parameters and identifying faults for each of the observable sub-sections; (v) calculating a global uncertainty value for each of the unobservable sub-sections; and (vi) repeating the steps (i)-(vi) iteratively, at each iteration selecting an uncertain sub-section of the water distribution network, until no uncertain sub-sections of the water distribution network remain.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.

Abstract: In an approach for adaptive sampling of smart meter data, a computer retrieves one or more balancing constraints associated with one or more smart meter sensors. The computer retrieves meter sensor data from the one or more smart meter sensors according to the one or more balancing constraints. The computer determines a subsample of the meter sensor data, and then transmits the subsample of the meter sensor data to an optimization engine for use in solving an optimization problem.